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Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Author

Listed:
  • Ben Li

    (Emory University)

  • Yunxiao Li

    (Emory University)

  • Zhaohui S. Qin

    (Emory University
    Emory University School of Medicine)

Abstract

Modern high-throughput biotechnologies such as microarray and next-generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical “large p, small n” problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in the Big Data era, large amount of historical data are available which should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model. Through simulation and real data analysis, we demonstrated superior performance of the proposed strategy. Our new strategy also enables borrowing information across different platforms which could be extremely useful with emergence of new technologies and accumulation of data from different platforms in the Big Data era. Our method has been implemented in R package “adaptiveHM,” which is freely available from https://github.com/benliemory/adaptiveHM.

Suggested Citation

  • Ben Li & Yunxiao Li & Zhaohui S. Qin, 2017. "Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 73-90, June.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9156-x
    DOI: 10.1007/s12561-016-9156-x
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    References listed on IDEAS

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    1. Xuekui Zhang & Gordon Robertson & Martin Krzywinski & Kaida Ning & Arnaud Droit & Steven Jones & Raphael Gottardo, 2011. "PICS: Probabilistic Inference for ChIP-seq," Biometrics, The International Biometric Society, vol. 67(1), pages 151-163, March.
    2. Brian P. Hobbs & Bradley P. Carlin & Sumithra J. Mandrekar & Daniel J. Sargent, 2011. "Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(3), pages 1047-1056, September.
    3. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    4. Zhaohui Qin & Ben Li & Karen N. Conneely & Hao Wu & Ming Hu & Deepak Ayyala & Yongseok Park & Victor X. Jin & Fangyuan Zhang & Han Zhang & Li Li & Shili Lin, 2016. "Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 284-309, October.
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